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<a href="#pub-types">Public 类型</a> &#124;
<a href="#pub-methods">Public 成员函数</a> &#124;
<a href="#pro-methods">Protected 成员函数</a> &#124;
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<div class="title">pcl::KdTree&lt; PointT &gt; 模板类 参考<span class="mlabels"><span class="mlabel">abstract</span></span></div>  </div>
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<p><a class="el" href="classpcl_1_1_kd_tree.html" title="KdTree represents the base spatial locator class for kd-tree implementations.">KdTree</a> represents the base spatial locator class for kd-tree implementations.  
 <a href="classpcl_1_1_kd_tree.html#details">更多...</a></p>

<p><code>#include &lt;<a class="el" href="kdtree_2include_2pcl_2kdtree_2kdtree_8h_source.html">kdtree.h</a>&gt;</code></p>
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类 pcl::KdTree&lt; PointT &gt; 继承关系图:</div>
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  <img src="classpcl_1_1_kd_tree.png" usemap="#pcl::KdTree_3C_20PointT_20_3E_map" alt=""/>
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<table class="memberdecls">
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Public 类型</h2></td></tr>
<tr class="memitem:ae7de862d46cb5f2d9b60fa8922f45d43"><td class="memItemLeft" align="right" valign="top"><a id="ae7de862d46cb5f2d9b60fa8922f45d43"></a>
typedef boost::shared_ptr&lt; std::vector&lt; int &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>IndicesPtr</b></td></tr>
<tr class="separator:ae7de862d46cb5f2d9b60fa8922f45d43"><td class="memSeparator" colspan="2">&#160;</td></tr>
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typedef boost::shared_ptr&lt; const std::vector&lt; int &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>IndicesConstPtr</b></td></tr>
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typedef <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloud</b></td></tr>
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<tr class="memitem:abea0b603234db8adbc67a125db20ed74"><td class="memItemLeft" align="right" valign="top"><a id="abea0b603234db8adbc67a125db20ed74"></a>
typedef boost::shared_ptr&lt; <a class="el" href="classpcl_1_1_point_cloud.html">PointCloud</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloudPtr</b></td></tr>
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typedef boost::shared_ptr&lt; const <a class="el" href="classpcl_1_1_point_cloud.html">PointCloud</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloudConstPtr</b></td></tr>
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<tr class="memitem:a637c567a0c5c457ddda56ebb209d3610"><td class="memItemLeft" align="right" valign="top"><a id="a637c567a0c5c457ddda56ebb209d3610"></a>
typedef <a class="el" href="classpcl_1_1_point_representation.html">pcl::PointRepresentation</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>PointRepresentation</b></td></tr>
<tr class="separator:a637c567a0c5c457ddda56ebb209d3610"><td class="memSeparator" colspan="2">&#160;</td></tr>
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typedef boost::shared_ptr&lt; const <a class="el" href="classpcl_1_1_point_representation.html">PointRepresentation</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>PointRepresentationConstPtr</b></td></tr>
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<tr class="memitem:aac67d73fe6e1949c885dee9f6ac2e318"><td class="memItemLeft" align="right" valign="top"><a id="aac67d73fe6e1949c885dee9f6ac2e318"></a>
typedef boost::shared_ptr&lt; <a class="el" href="classpcl_1_1_kd_tree.html">KdTree</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>Ptr</b></td></tr>
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<tr class="memitem:a07d45964a3e71f92234ee32848b6e78f"><td class="memItemLeft" align="right" valign="top"><a id="a07d45964a3e71f92234ee32848b6e78f"></a>
typedef boost::shared_ptr&lt; const <a class="el" href="classpcl_1_1_kd_tree.html">KdTree</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>ConstPtr</b></td></tr>
<tr class="separator:a07d45964a3e71f92234ee32848b6e78f"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public 成员函数</h2></td></tr>
<tr class="memitem:a49e8890a0cd0e35d0d5290b6e2be7900"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#a49e8890a0cd0e35d0d5290b6e2be7900">KdTree</a> (bool sorted=true)</td></tr>
<tr class="memdesc:a49e8890a0cd0e35d0d5290b6e2be7900"><td class="mdescLeft">&#160;</td><td class="mdescRight">Empty constructor for <a class="el" href="classpcl_1_1_kd_tree.html" title="KdTree represents the base spatial locator class for kd-tree implementations.">KdTree</a>. Sets some internal values to their defaults.  <a href="classpcl_1_1_kd_tree.html#a49e8890a0cd0e35d0d5290b6e2be7900">更多...</a><br /></td></tr>
<tr class="separator:a49e8890a0cd0e35d0d5290b6e2be7900"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac105d90b2b10383adb58e62abe7b1161"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#ac105d90b2b10383adb58e62abe7b1161">setInputCloud</a> (const PointCloudConstPtr &amp;cloud, const IndicesConstPtr &amp;indices=IndicesConstPtr())</td></tr>
<tr class="memdesc:ac105d90b2b10383adb58e62abe7b1161"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provide a pointer to the input dataset.  <a href="classpcl_1_1_kd_tree.html#ac105d90b2b10383adb58e62abe7b1161">更多...</a><br /></td></tr>
<tr class="separator:ac105d90b2b10383adb58e62abe7b1161"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a5ea7020b3505f736ba78fb38be00d16a"><td class="memItemLeft" align="right" valign="top"><a id="a5ea7020b3505f736ba78fb38be00d16a"></a>
IndicesConstPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#a5ea7020b3505f736ba78fb38be00d16a">getIndices</a> () const</td></tr>
<tr class="memdesc:a5ea7020b3505f736ba78fb38be00d16a"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get a pointer to the vector of indices used. <br /></td></tr>
<tr class="separator:a5ea7020b3505f736ba78fb38be00d16a"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a4839876a6d01bddc7984a3193e23d463"><td class="memItemLeft" align="right" valign="top"><a id="a4839876a6d01bddc7984a3193e23d463"></a>
PointCloudConstPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#a4839876a6d01bddc7984a3193e23d463">getInputCloud</a> () const</td></tr>
<tr class="memdesc:a4839876a6d01bddc7984a3193e23d463"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get a pointer to the input point cloud dataset. <br /></td></tr>
<tr class="separator:a4839876a6d01bddc7984a3193e23d463"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ab2c8cd07baaebb4e1504f223405419cc"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#ab2c8cd07baaebb4e1504f223405419cc">setPointRepresentation</a> (const PointRepresentationConstPtr &amp;point_representation)</td></tr>
<tr class="memdesc:ab2c8cd07baaebb4e1504f223405419cc"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provide a pointer to the point representation to use to convert points into k-D vectors.  <a href="classpcl_1_1_kd_tree.html#ab2c8cd07baaebb4e1504f223405419cc">更多...</a><br /></td></tr>
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PointRepresentationConstPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#afa158ec6aedf91fc898fadffdee449c6">getPointRepresentation</a> () const</td></tr>
<tr class="memdesc:afa158ec6aedf91fc898fadffdee449c6"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get a pointer to the point representation used when converting points into k-D vectors. <br /></td></tr>
<tr class="separator:afa158ec6aedf91fc898fadffdee449c6"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aec3baf44b02605ba4efde9f49e93db3d"><td class="memItemLeft" align="right" valign="top"><a id="aec3baf44b02605ba4efde9f49e93db3d"></a>
virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#aec3baf44b02605ba4efde9f49e93db3d">~KdTree</a> ()</td></tr>
<tr class="memdesc:aec3baf44b02605ba4efde9f49e93db3d"><td class="mdescLeft">&#160;</td><td class="mdescRight">Destructor for <a class="el" href="classpcl_1_1_kd_tree.html" title="KdTree represents the base spatial locator class for kd-tree implementations.">KdTree</a>. Deletes all allocated data arrays and destroys the kd-tree structures. <br /></td></tr>
<tr class="separator:aec3baf44b02605ba4efde9f49e93db3d"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac81c442ff9c9b1e03c10cb55128e726d"><td class="memItemLeft" align="right" valign="top">virtual int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#ac81c442ff9c9b1e03c10cb55128e726d">nearestKSearch</a> (const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;p_q, int k, std::vector&lt; int &gt; &amp;k_indices, std::vector&lt; float &gt; &amp;k_sqr_distances) const =0</td></tr>
<tr class="memdesc:ac81c442ff9c9b1e03c10cb55128e726d"><td class="mdescLeft">&#160;</td><td class="mdescRight">Search for k-nearest neighbors for the given query point.  <a href="classpcl_1_1_kd_tree.html#ac81c442ff9c9b1e03c10cb55128e726d">更多...</a><br /></td></tr>
<tr class="separator:ac81c442ff9c9b1e03c10cb55128e726d"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a6375c3f23775693f316482e7bd1c5e5d"><td class="memItemLeft" align="right" valign="top">virtual int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#a6375c3f23775693f316482e7bd1c5e5d">nearestKSearch</a> (const <a class="el" href="classpcl_1_1_point_cloud.html">PointCloud</a> &amp;cloud, int index, int k, std::vector&lt; int &gt; &amp;k_indices, std::vector&lt; float &gt; &amp;k_sqr_distances) const</td></tr>
<tr class="memdesc:a6375c3f23775693f316482e7bd1c5e5d"><td class="mdescLeft">&#160;</td><td class="mdescRight">Search for k-nearest neighbors for the given query point.  <a href="classpcl_1_1_kd_tree.html#a6375c3f23775693f316482e7bd1c5e5d">更多...</a><br /></td></tr>
<tr class="separator:a6375c3f23775693f316482e7bd1c5e5d"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a3c3de00ef91b96c2680c17de1b236c23"><td class="memTemplParams" colspan="2">template&lt;typename PointTDiff &gt; </td></tr>
<tr class="memitem:a3c3de00ef91b96c2680c17de1b236c23"><td class="memTemplItemLeft" align="right" valign="top">int&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#a3c3de00ef91b96c2680c17de1b236c23">nearestKSearchT</a> (const PointTDiff &amp;point, int k, std::vector&lt; int &gt; &amp;k_indices, std::vector&lt; float &gt; &amp;k_sqr_distances) const</td></tr>
<tr class="memdesc:a3c3de00ef91b96c2680c17de1b236c23"><td class="mdescLeft">&#160;</td><td class="mdescRight">Search for k-nearest neighbors for the given query point. This method accepts a different template parameter for the point type.  <a href="classpcl_1_1_kd_tree.html#a3c3de00ef91b96c2680c17de1b236c23">更多...</a><br /></td></tr>
<tr class="separator:a3c3de00ef91b96c2680c17de1b236c23"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7b8a30dcd9117c962e1940ad2bf3b79d"><td class="memItemLeft" align="right" valign="top">virtual int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#a7b8a30dcd9117c962e1940ad2bf3b79d">nearestKSearch</a> (int index, int k, std::vector&lt; int &gt; &amp;k_indices, std::vector&lt; float &gt; &amp;k_sqr_distances) const</td></tr>
<tr class="memdesc:a7b8a30dcd9117c962e1940ad2bf3b79d"><td class="mdescLeft">&#160;</td><td class="mdescRight">Search for k-nearest neighbors for the given query point (zero-copy).  <a href="classpcl_1_1_kd_tree.html#a7b8a30dcd9117c962e1940ad2bf3b79d">更多...</a><br /></td></tr>
<tr class="separator:a7b8a30dcd9117c962e1940ad2bf3b79d"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a662d9de50237121e142502a8737dfefa"><td class="memItemLeft" align="right" valign="top">virtual int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#a662d9de50237121e142502a8737dfefa">radiusSearch</a> (const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;p_q, double radius, std::vector&lt; int &gt; &amp;k_indices, std::vector&lt; float &gt; &amp;k_sqr_distances, unsigned int max_nn=0) const =0</td></tr>
<tr class="memdesc:a662d9de50237121e142502a8737dfefa"><td class="mdescLeft">&#160;</td><td class="mdescRight">Search for all the nearest neighbors of the query point in a given radius.  <a href="classpcl_1_1_kd_tree.html#a662d9de50237121e142502a8737dfefa">更多...</a><br /></td></tr>
<tr class="separator:a662d9de50237121e142502a8737dfefa"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a22292d6936e364d71b9289e5d8d58b1c"><td class="memItemLeft" align="right" valign="top">virtual int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#a22292d6936e364d71b9289e5d8d58b1c">radiusSearch</a> (const <a class="el" href="classpcl_1_1_point_cloud.html">PointCloud</a> &amp;cloud, int index, double radius, std::vector&lt; int &gt; &amp;k_indices, std::vector&lt; float &gt; &amp;k_sqr_distances, unsigned int max_nn=0) const</td></tr>
<tr class="memdesc:a22292d6936e364d71b9289e5d8d58b1c"><td class="mdescLeft">&#160;</td><td class="mdescRight">Search for all the nearest neighbors of the query point in a given radius.  <a href="classpcl_1_1_kd_tree.html#a22292d6936e364d71b9289e5d8d58b1c">更多...</a><br /></td></tr>
<tr class="separator:a22292d6936e364d71b9289e5d8d58b1c"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aa55c4339c96e4e406477418a1c98f289"><td class="memTemplParams" colspan="2">template&lt;typename PointTDiff &gt; </td></tr>
<tr class="memitem:aa55c4339c96e4e406477418a1c98f289"><td class="memTemplItemLeft" align="right" valign="top">int&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#aa55c4339c96e4e406477418a1c98f289">radiusSearchT</a> (const PointTDiff &amp;point, double radius, std::vector&lt; int &gt; &amp;k_indices, std::vector&lt; float &gt; &amp;k_sqr_distances, unsigned int max_nn=0) const</td></tr>
<tr class="memdesc:aa55c4339c96e4e406477418a1c98f289"><td class="mdescLeft">&#160;</td><td class="mdescRight">Search for all the nearest neighbors of the query point in a given radius.  <a href="classpcl_1_1_kd_tree.html#aa55c4339c96e4e406477418a1c98f289">更多...</a><br /></td></tr>
<tr class="separator:aa55c4339c96e4e406477418a1c98f289"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aa98483c78ce77e07454a9bfe56839cd4"><td class="memItemLeft" align="right" valign="top">virtual int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#aa98483c78ce77e07454a9bfe56839cd4">radiusSearch</a> (int index, double radius, std::vector&lt; int &gt; &amp;k_indices, std::vector&lt; float &gt; &amp;k_sqr_distances, unsigned int max_nn=0) const</td></tr>
<tr class="memdesc:aa98483c78ce77e07454a9bfe56839cd4"><td class="mdescLeft">&#160;</td><td class="mdescRight">Search for all the nearest neighbors of the query point in a given radius (zero-copy).  <a href="classpcl_1_1_kd_tree.html#aa98483c78ce77e07454a9bfe56839cd4">更多...</a><br /></td></tr>
<tr class="separator:aa98483c78ce77e07454a9bfe56839cd4"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aa150954080bc14f46772871739c3bfff"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#aa150954080bc14f46772871739c3bfff">setEpsilon</a> (float eps)</td></tr>
<tr class="memdesc:aa150954080bc14f46772871739c3bfff"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the search epsilon precision (error bound) for nearest neighbors searches.  <a href="classpcl_1_1_kd_tree.html#aa150954080bc14f46772871739c3bfff">更多...</a><br /></td></tr>
<tr class="separator:aa150954080bc14f46772871739c3bfff"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a9f29d3de4ab5a0213806077e7c280f1b"><td class="memItemLeft" align="right" valign="top"><a id="a9f29d3de4ab5a0213806077e7c280f1b"></a>
float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#a9f29d3de4ab5a0213806077e7c280f1b">getEpsilon</a> () const</td></tr>
<tr class="memdesc:a9f29d3de4ab5a0213806077e7c280f1b"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the search epsilon precision (error bound) for nearest neighbors searches. <br /></td></tr>
<tr class="separator:a9f29d3de4ab5a0213806077e7c280f1b"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:abd7f2b98e375c48d9fe113b95b3edc20"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#abd7f2b98e375c48d9fe113b95b3edc20">setMinPts</a> (int min_pts)</td></tr>
<tr class="memdesc:abd7f2b98e375c48d9fe113b95b3edc20"><td class="mdescLeft">&#160;</td><td class="mdescRight">Minimum allowed number of k nearest neighbors points that a viable result must contain.  <a href="classpcl_1_1_kd_tree.html#abd7f2b98e375c48d9fe113b95b3edc20">更多...</a><br /></td></tr>
<tr class="separator:abd7f2b98e375c48d9fe113b95b3edc20"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac62ad92ed0a7a494cdb8ba52d2c0dc08"><td class="memItemLeft" align="right" valign="top"><a id="ac62ad92ed0a7a494cdb8ba52d2c0dc08"></a>
int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#ac62ad92ed0a7a494cdb8ba52d2c0dc08">getMinPts</a> () const</td></tr>
<tr class="memdesc:ac62ad92ed0a7a494cdb8ba52d2c0dc08"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the minimum allowed number of k nearest neighbors points that a viable result must contain. <br /></td></tr>
<tr class="separator:ac62ad92ed0a7a494cdb8ba52d2c0dc08"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pro-methods"></a>
Protected 成员函数</h2></td></tr>
<tr class="memitem:af2adf6e714a116858487e4d40859d80e"><td class="memItemLeft" align="right" valign="top"><a id="af2adf6e714a116858487e4d40859d80e"></a>
virtual std::string&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#af2adf6e714a116858487e4d40859d80e">getName</a> () const =0</td></tr>
<tr class="memdesc:af2adf6e714a116858487e4d40859d80e"><td class="mdescLeft">&#160;</td><td class="mdescRight">Class getName method. <br /></td></tr>
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</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pro-attribs"></a>
Protected 属性</h2></td></tr>
<tr class="memitem:a9b71072db4f7662c7c565d4c49145db2"><td class="memItemLeft" align="right" valign="top"><a id="a9b71072db4f7662c7c565d4c49145db2"></a>
PointCloudConstPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#a9b71072db4f7662c7c565d4c49145db2">input_</a></td></tr>
<tr class="memdesc:a9b71072db4f7662c7c565d4c49145db2"><td class="mdescLeft">&#160;</td><td class="mdescRight">The input point cloud dataset containing the points we need to use. <br /></td></tr>
<tr class="separator:a9b71072db4f7662c7c565d4c49145db2"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae59a4b07f95b8193d951f940f7fb6e1d"><td class="memItemLeft" align="right" valign="top"><a id="ae59a4b07f95b8193d951f940f7fb6e1d"></a>
IndicesConstPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#ae59a4b07f95b8193d951f940f7fb6e1d">indices_</a></td></tr>
<tr class="memdesc:ae59a4b07f95b8193d951f940f7fb6e1d"><td class="mdescLeft">&#160;</td><td class="mdescRight">A pointer to the vector of point indices to use. <br /></td></tr>
<tr class="separator:ae59a4b07f95b8193d951f940f7fb6e1d"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aa393b60f0978c529b28e060a21c96222"><td class="memItemLeft" align="right" valign="top"><a id="aa393b60f0978c529b28e060a21c96222"></a>
float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#aa393b60f0978c529b28e060a21c96222">epsilon_</a></td></tr>
<tr class="memdesc:aa393b60f0978c529b28e060a21c96222"><td class="mdescLeft">&#160;</td><td class="mdescRight">Epsilon precision (error bound) for nearest neighbors searches. <br /></td></tr>
<tr class="separator:aa393b60f0978c529b28e060a21c96222"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aaceb7706d5a8b57ab626277f826162d3"><td class="memItemLeft" align="right" valign="top"><a id="aaceb7706d5a8b57ab626277f826162d3"></a>
int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#aaceb7706d5a8b57ab626277f826162d3">min_pts_</a></td></tr>
<tr class="memdesc:aaceb7706d5a8b57ab626277f826162d3"><td class="mdescLeft">&#160;</td><td class="mdescRight">Minimum allowed number of k nearest neighbors points that a viable result must contain. <br /></td></tr>
<tr class="separator:aaceb7706d5a8b57ab626277f826162d3"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac50d9f0a88e43cb9be347337865a5194"><td class="memItemLeft" align="right" valign="top"><a id="ac50d9f0a88e43cb9be347337865a5194"></a>
bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#ac50d9f0a88e43cb9be347337865a5194">sorted_</a></td></tr>
<tr class="memdesc:ac50d9f0a88e43cb9be347337865a5194"><td class="mdescLeft">&#160;</td><td class="mdescRight">Return the radius search neighbours sorted <br /></td></tr>
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<tr class="memitem:a2cacacf162468a473dca65193e708002"><td class="memItemLeft" align="right" valign="top"><a id="a2cacacf162468a473dca65193e708002"></a>
PointRepresentationConstPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_kd_tree.html#a2cacacf162468a473dca65193e708002">point_representation_</a></td></tr>
<tr class="memdesc:a2cacacf162468a473dca65193e708002"><td class="mdescLeft">&#160;</td><td class="mdescRight">For converting different point structures into k-dimensional vectors for nearest-neighbor search. <br /></td></tr>
<tr class="separator:a2cacacf162468a473dca65193e708002"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">详细描述</h2>
<div class="textblock"><h3>template&lt;typename PointT&gt;<br />
class pcl::KdTree&lt; PointT &gt;</h3>

<p><a class="el" href="classpcl_1_1_kd_tree.html" title="KdTree represents the base spatial locator class for kd-tree implementations.">KdTree</a> represents the base spatial locator class for kd-tree implementations. </p>
<dl class="section author"><dt>作者</dt><dd>Radu B Rusu, Bastian Steder, Michael Dixon </dd></dl>
</div><h2 class="groupheader">构造及析构函数说明</h2>
<a id="a49e8890a0cd0e35d0d5290b6e2be7900"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a49e8890a0cd0e35d0d5290b6e2be7900">&#9670;&nbsp;</a></span>KdTree()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
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          <td class="memname"><a class="el" href="classpcl_1_1_kd_tree.html">pcl::KdTree</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::<a class="el" href="classpcl_1_1_kd_tree.html">KdTree</a> </td>
          <td>(</td>
          <td class="paramtype">bool&#160;</td>
          <td class="paramname"><em>sorted</em> = <code>true</code></td><td>)</td>
          <td></td>
        </tr>
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  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
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<p>Empty constructor for <a class="el" href="classpcl_1_1_kd_tree.html" title="KdTree represents the base spatial locator class for kd-tree implementations.">KdTree</a>. Sets some internal values to their defaults. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">sorted</td><td>set to true if the application that the tree will be used for requires sorted nearest neighbor indices (default). False otherwise. </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;                                  : <a class="code" href="classpcl_1_1_kd_tree.html#a9b71072db4f7662c7c565d4c49145db2">input_</a>(), <a class="code" href="classpcl_1_1_kd_tree.html#ae59a4b07f95b8193d951f940f7fb6e1d">indices_</a>(), </div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;                                    <a class="code" href="classpcl_1_1_kd_tree.html#aa393b60f0978c529b28e060a21c96222">epsilon_</a>(0.0f), <a class="code" href="classpcl_1_1_kd_tree.html#aaceb7706d5a8b57ab626277f826162d3">min_pts_</a>(1), <a class="code" href="classpcl_1_1_kd_tree.html#ac50d9f0a88e43cb9be347337865a5194">sorted_</a>(sorted), </div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;                                    <a class="code" href="classpcl_1_1_kd_tree.html#a2cacacf162468a473dca65193e708002">point_representation_</a> (<span class="keyword">new</span> DefaultPointRepresentation&lt;PointT&gt;)</div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;      {</div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;      };</div>
<div class="ttc" id="aclasspcl_1_1_kd_tree_html_a2cacacf162468a473dca65193e708002"><div class="ttname"><a href="classpcl_1_1_kd_tree.html#a2cacacf162468a473dca65193e708002">pcl::KdTree::point_representation_</a></div><div class="ttdeci">PointRepresentationConstPtr point_representation_</div><div class="ttdoc">For converting different point structures into k-dimensional vectors for nearest-neighbor search.</div><div class="ttdef"><b>Definition:</b> kdtree.h:359</div></div>
<div class="ttc" id="aclasspcl_1_1_kd_tree_html_a9b71072db4f7662c7c565d4c49145db2"><div class="ttname"><a href="classpcl_1_1_kd_tree.html#a9b71072db4f7662c7c565d4c49145db2">pcl::KdTree::input_</a></div><div class="ttdeci">PointCloudConstPtr input_</div><div class="ttdoc">The input point cloud dataset containing the points we need to use.</div><div class="ttdef"><b>Definition:</b> kdtree.h:344</div></div>
<div class="ttc" id="aclasspcl_1_1_kd_tree_html_aa393b60f0978c529b28e060a21c96222"><div class="ttname"><a href="classpcl_1_1_kd_tree.html#aa393b60f0978c529b28e060a21c96222">pcl::KdTree::epsilon_</a></div><div class="ttdeci">float epsilon_</div><div class="ttdoc">Epsilon precision (error bound) for nearest neighbors searches.</div><div class="ttdef"><b>Definition:</b> kdtree.h:350</div></div>
<div class="ttc" id="aclasspcl_1_1_kd_tree_html_aaceb7706d5a8b57ab626277f826162d3"><div class="ttname"><a href="classpcl_1_1_kd_tree.html#aaceb7706d5a8b57ab626277f826162d3">pcl::KdTree::min_pts_</a></div><div class="ttdeci">int min_pts_</div><div class="ttdoc">Minimum allowed number of k nearest neighbors points that a viable result must contain.</div><div class="ttdef"><b>Definition:</b> kdtree.h:353</div></div>
<div class="ttc" id="aclasspcl_1_1_kd_tree_html_ac50d9f0a88e43cb9be347337865a5194"><div class="ttname"><a href="classpcl_1_1_kd_tree.html#ac50d9f0a88e43cb9be347337865a5194">pcl::KdTree::sorted_</a></div><div class="ttdeci">bool sorted_</div><div class="ttdoc">Return the radius search neighbours sorted</div><div class="ttdef"><b>Definition:</b> kdtree.h:356</div></div>
<div class="ttc" id="aclasspcl_1_1_kd_tree_html_ae59a4b07f95b8193d951f940f7fb6e1d"><div class="ttname"><a href="classpcl_1_1_kd_tree.html#ae59a4b07f95b8193d951f940f7fb6e1d">pcl::KdTree::indices_</a></div><div class="ttdeci">IndicesConstPtr indices_</div><div class="ttdoc">A pointer to the vector of point indices to use.</div><div class="ttdef"><b>Definition:</b> kdtree.h:347</div></div>
</div><!-- fragment -->
</div>
</div>
<h2 class="groupheader">成员函数说明</h2>
<a id="a6375c3f23775693f316482e7bd1c5e5d"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a6375c3f23775693f316482e7bd1c5e5d">&#9670;&nbsp;</a></span>nearestKSearch() <span class="overload">[1/3]</span></h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
<table class="mlabels">
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  <td class="mlabels-left">
      <table class="memname">
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          <td class="memname">virtual int <a class="el" href="classpcl_1_1_kd_tree.html">pcl::KdTree</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::nearestKSearch </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="classpcl_1_1_point_cloud.html">PointCloud</a> &amp;&#160;</td>
          <td class="paramname"><em>cloud</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>index</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>k</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; int &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_indices</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_sqr_distances</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
  </td>
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<span class="mlabels"><span class="mlabel">inline</span><span class="mlabel">virtual</span></span>  </td>
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<p>Search for k-nearest neighbors for the given query point. </p>
<dl class="section attention"><dt>注意</dt><dd>This method does not do any bounds checking for the input index (i.e., index &gt;= cloud.points.size () || index &lt; 0), and assumes valid (i.e., finite) data.</dd></dl>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">cloud</td><td>the point cloud data </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">index</td><td>a <em>valid</em> index in <em>cloud</em> representing a <em>valid</em> (i.e., finite) query point </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">k</td><td>the number of neighbors to search for </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">k_indices</td><td>the resultant indices of the neighboring points (must be resized to <em>k</em> a priori!) </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">k_sqr_distances</td><td>the resultant squared distances to the neighboring points (must be resized to <em>k</em> a priori!)</td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>返回</dt><dd>number of neighbors found</dd></dl>
<dl class="exception"><dt>异常</dt><dd>
  <table class="exception">
    <tr><td class="paramname">asserts</td><td>in debug mode if the index is not between 0 and the maximum number of points </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;      {</div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;        assert (index &gt;= 0 &amp;&amp; index &lt; <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ()) &amp;&amp; <span class="stringliteral">&quot;Out-of-bounds error in nearestKSearch!&quot;</span>);</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;        <span class="keywordflow">return</span> (<a class="code" href="classpcl_1_1_kd_tree.html#ac81c442ff9c9b1e03c10cb55128e726d">nearestKSearch</a> (cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[index], k, k_indices, k_sqr_distances));</div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;      }</div>
<div class="ttc" id="aclasspcl_1_1_kd_tree_html_ac81c442ff9c9b1e03c10cb55128e726d"><div class="ttname"><a href="classpcl_1_1_kd_tree.html#ac81c442ff9c9b1e03c10cb55128e726d">pcl::KdTree::nearestKSearch</a></div><div class="ttdeci">virtual int nearestKSearch(const PointT &amp;p_q, int k, std::vector&lt; int &gt; &amp;k_indices, std::vector&lt; float &gt; &amp;k_sqr_distances) const =0</div><div class="ttdoc">Search for k-nearest neighbors for the given query point.</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_af16a62638198313b9c093127c492c884"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">pcl::PointCloud::points</a></div><div class="ttdeci">std::vector&lt; PointT, Eigen::aligned_allocator&lt; PointT &gt; &gt; points</div><div class="ttdoc">The point data.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:410</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#ac81c442ff9c9b1e03c10cb55128e726d">&#9670;&nbsp;</a></span>nearestKSearch() <span class="overload">[2/3]</span></h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual int <a class="el" href="classpcl_1_1_kd_tree.html">pcl::KdTree</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::nearestKSearch </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;&#160;</td>
          <td class="paramname"><em>p_q</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>k</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; int &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_indices</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_sqr_distances</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Search for k-nearest neighbors for the given query point. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">p_q</td><td>the given query point </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">k</td><td>the number of neighbors to search for </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">k_indices</td><td>the resultant indices of the neighboring points (must be resized to <em>k</em> a priori!) </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">k_sqr_distances</td><td>the resultant squared distances to the neighboring points (must be resized to <em>k</em> a priori!) </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>返回</dt><dd>number of neighbors found </dd></dl>

<p>在 <a class="el" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#a9bdbc03758c8d7b3033139e2fb1e6150">pcl::KdTreeFLANN&lt; PointTarget &gt;</a>, <a class="el" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#a9bdbc03758c8d7b3033139e2fb1e6150">pcl::KdTreeFLANN&lt; PointT, Dist &gt;</a>, <a class="el" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#a9bdbc03758c8d7b3033139e2fb1e6150">pcl::KdTreeFLANN&lt; pcl::VFHSignature308 &gt;</a>, <a class="el" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#a9bdbc03758c8d7b3033139e2fb1e6150">pcl::KdTreeFLANN&lt; pcl::PointXYZRGBA &gt;</a>, <a class="el" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#a9bdbc03758c8d7b3033139e2fb1e6150">pcl::KdTreeFLANN&lt; pcl::InterestPoint &gt;</a> , 以及 <a class="el" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#a9bdbc03758c8d7b3033139e2fb1e6150">pcl::KdTreeFLANN&lt; FeatureT &gt;</a> 内被实现.</p>

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<a id="a7b8a30dcd9117c962e1940ad2bf3b79d"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a7b8a30dcd9117c962e1940ad2bf3b79d">&#9670;&nbsp;</a></span>nearestKSearch() <span class="overload">[3/3]</span></h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual int <a class="el" href="classpcl_1_1_kd_tree.html">pcl::KdTree</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::nearestKSearch </td>
          <td>(</td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>index</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>k</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; int &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_indices</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_sqr_distances</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span><span class="mlabel">virtual</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Search for k-nearest neighbors for the given query point (zero-copy). </p>
<dl class="section attention"><dt>注意</dt><dd>This method does not do any bounds checking for the input index (i.e., index &gt;= cloud.points.size () || index &lt; 0), and assumes valid (i.e., finite) data.</dd></dl>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">index</td><td>a <em>valid</em> index representing a <em>valid</em> query point in the dataset given by <em>setInputCloud</em>. If indices were given in setInputCloud, index will be the position in the indices vector.</td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">k</td><td>the number of neighbors to search for </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">k_indices</td><td>the resultant indices of the neighboring points (must be resized to <em>k</em> a priori!) </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">k_sqr_distances</td><td>the resultant squared distances to the neighboring points (must be resized to <em>k</em> a priori!) </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>返回</dt><dd>number of neighbors found</dd></dl>
<dl class="exception"><dt>异常</dt><dd>
  <table class="exception">
    <tr><td class="paramname">asserts</td><td>in debug mode if the index is not between 0 and the maximum number of points </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;      {</div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;        <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_kd_tree.html#ae59a4b07f95b8193d951f940f7fb6e1d">indices_</a> == NULL)</div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;        {</div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;          assert (index &gt;= 0 &amp;&amp; index &lt; <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (<a class="code" href="classpcl_1_1_kd_tree.html#a9b71072db4f7662c7c565d4c49145db2">input_</a>-&gt;points.size ()) &amp;&amp; <span class="stringliteral">&quot;Out-of-bounds error in nearestKSearch!&quot;</span>);</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;          <span class="keywordflow">return</span> (<a class="code" href="classpcl_1_1_kd_tree.html#ac81c442ff9c9b1e03c10cb55128e726d">nearestKSearch</a> (<a class="code" href="classpcl_1_1_kd_tree.html#a9b71072db4f7662c7c565d4c49145db2">input_</a>-&gt;points[index], k, k_indices, k_sqr_distances));</div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;        }</div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;        <span class="keywordflow">else</span></div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;        {</div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;          assert (index &gt;= 0 &amp;&amp; index &lt; <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (<a class="code" href="classpcl_1_1_kd_tree.html#ae59a4b07f95b8193d951f940f7fb6e1d">indices_</a>-&gt;size ()) &amp;&amp; <span class="stringliteral">&quot;Out-of-bounds error in nearestKSearch!&quot;</span>);</div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;          <span class="keywordflow">return</span> (<a class="code" href="classpcl_1_1_kd_tree.html#ac81c442ff9c9b1e03c10cb55128e726d">nearestKSearch</a> (<a class="code" href="classpcl_1_1_kd_tree.html#a9b71072db4f7662c7c565d4c49145db2">input_</a>-&gt;points[(*<a class="code" href="classpcl_1_1_kd_tree.html#ae59a4b07f95b8193d951f940f7fb6e1d">indices_</a>)[index]], k, k_indices, k_sqr_distances));</div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;        }</div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a3c3de00ef91b96c2680c17de1b236c23">&#9670;&nbsp;</a></span>nearestKSearchT()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
<div class="memtemplate">
template&lt;typename PointTDiff &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">int <a class="el" href="classpcl_1_1_kd_tree.html">pcl::KdTree</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::nearestKSearchT </td>
          <td>(</td>
          <td class="paramtype">const PointTDiff &amp;&#160;</td>
          <td class="paramname"><em>point</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>k</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; int &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_indices</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_sqr_distances</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Search for k-nearest neighbors for the given query point. This method accepts a different template parameter for the point type. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">point</td><td>the given query point </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">k</td><td>the number of neighbors to search for </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">k_indices</td><td>the resultant indices of the neighboring points (must be resized to <em>k</em> a priori!) </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">k_sqr_distances</td><td>the resultant squared distances to the neighboring points (must be resized to <em>k</em> a priori!) </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>返回</dt><dd>number of neighbors found </dd></dl>
<div class="fragment"><div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;      {</div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;        <a class="code" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> p;</div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;        <a class="code" href="group__common.html#gab978bf1754771246b2f140a5b52a8f8b">copyPoint</a> (point, p);</div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;        <span class="keywordflow">return</span> (<a class="code" href="classpcl_1_1_kd_tree.html#ac81c442ff9c9b1e03c10cb55128e726d">nearestKSearch</a> (p, k, k_indices, k_sqr_distances));</div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;      }</div>
<div class="ttc" id="agroup__common_html_gab978bf1754771246b2f140a5b52a8f8b"><div class="ttname"><a href="group__common.html#gab978bf1754771246b2f140a5b52a8f8b">pcl::copyPoint</a></div><div class="ttdeci">void copyPoint(const PointInT &amp;point_in, PointOutT &amp;point_out)</div><div class="ttdoc">Copy the fields of a source point into a target point.</div><div class="ttdef"><b>Definition:</b> copy_point.hpp:138</div></div>
<div class="ttc" id="astructpcl_1_1_point_x_y_z_r_g_b_a_html"><div class="ttname"><a href="structpcl_1_1_point_x_y_z_r_g_b_a.html">pcl::PointXYZRGBA</a></div><div class="ttdoc">A point structure representing Euclidean xyz coordinates, and the RGBA color.</div><div class="ttdef"><b>Definition:</b> point_types.hpp:540</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a22292d6936e364d71b9289e5d8d58b1c">&#9670;&nbsp;</a></span>radiusSearch() <span class="overload">[1/3]</span></h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual int <a class="el" href="classpcl_1_1_kd_tree.html">pcl::KdTree</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::radiusSearch </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="classpcl_1_1_point_cloud.html">PointCloud</a> &amp;&#160;</td>
          <td class="paramname"><em>cloud</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>index</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">double&#160;</td>
          <td class="paramname"><em>radius</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; int &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_indices</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_sqr_distances</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">unsigned int&#160;</td>
          <td class="paramname"><em>max_nn</em> = <code>0</code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span><span class="mlabel">virtual</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Search for all the nearest neighbors of the query point in a given radius. </p>
<dl class="section attention"><dt>注意</dt><dd>This method does not do any bounds checking for the input index (i.e., index &gt;= cloud.points.size () || index &lt; 0), and assumes valid (i.e., finite) data.</dd></dl>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">cloud</td><td>the point cloud data </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">index</td><td>a <em>valid</em> index in <em>cloud</em> representing a <em>valid</em> (i.e., finite) query point </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">radius</td><td>the radius of the sphere bounding all of p_q's neighbors </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">k_indices</td><td>the resultant indices of the neighboring points </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">k_sqr_distances</td><td>the resultant squared distances to the neighboring points </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">max_nn</td><td>if given, bounds the maximum returned neighbors to this value. If <em>max_nn</em> is set to 0 or to a number higher than the number of points in the input cloud, all neighbors in <em>radius</em> will be returned. </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>返回</dt><dd>number of neighbors found in radius</dd></dl>
<dl class="exception"><dt>异常</dt><dd>
  <table class="exception">
    <tr><td class="paramname">asserts</td><td>in debug mode if the index is not between 0 and the maximum number of points </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;      {</div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;        assert (index &gt;= 0 &amp;&amp; index &lt; <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ()) &amp;&amp; <span class="stringliteral">&quot;Out-of-bounds error in radiusSearch!&quot;</span>);</div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;        <span class="keywordflow">return</span> (<a class="code" href="classpcl_1_1_kd_tree.html#a662d9de50237121e142502a8737dfefa">radiusSearch</a>(cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[index], radius, k_indices, k_sqr_distances, max_nn));</div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;      }</div>
<div class="ttc" id="aclasspcl_1_1_kd_tree_html_a662d9de50237121e142502a8737dfefa"><div class="ttname"><a href="classpcl_1_1_kd_tree.html#a662d9de50237121e142502a8737dfefa">pcl::KdTree::radiusSearch</a></div><div class="ttdeci">virtual int radiusSearch(const PointT &amp;p_q, double radius, std::vector&lt; int &gt; &amp;k_indices, std::vector&lt; float &gt; &amp;k_sqr_distances, unsigned int max_nn=0) const =0</div><div class="ttdoc">Search for all the nearest neighbors of the query point in a given radius.</div></div>
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<a id="a662d9de50237121e142502a8737dfefa"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a662d9de50237121e142502a8737dfefa">&#9670;&nbsp;</a></span>radiusSearch() <span class="overload">[2/3]</span></h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual int <a class="el" href="classpcl_1_1_kd_tree.html">pcl::KdTree</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::radiusSearch </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;&#160;</td>
          <td class="paramname"><em>p_q</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">double&#160;</td>
          <td class="paramname"><em>radius</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; int &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_indices</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_sqr_distances</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">unsigned int&#160;</td>
          <td class="paramname"><em>max_nn</em> = <code>0</code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">pure virtual</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Search for all the nearest neighbors of the query point in a given radius. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">p_q</td><td>the given query point </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">radius</td><td>the radius of the sphere bounding all of p_q's neighbors </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">k_indices</td><td>the resultant indices of the neighboring points </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">k_sqr_distances</td><td>the resultant squared distances to the neighboring points </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">max_nn</td><td>if given, bounds the maximum returned neighbors to this value. If <em>max_nn</em> is set to 0 or to a number higher than the number of points in the input cloud, all neighbors in <em>radius</em> will be returned. </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>返回</dt><dd>number of neighbors found in radius </dd></dl>

<p>在 <a class="el" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#ab598d8e1220f1292b938e3a66f1ec370">pcl::KdTreeFLANN&lt; PointTarget &gt;</a>, <a class="el" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#ab598d8e1220f1292b938e3a66f1ec370">pcl::KdTreeFLANN&lt; PointT, Dist &gt;</a>, <a class="el" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#ab598d8e1220f1292b938e3a66f1ec370">pcl::KdTreeFLANN&lt; pcl::VFHSignature308 &gt;</a>, <a class="el" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#ab598d8e1220f1292b938e3a66f1ec370">pcl::KdTreeFLANN&lt; pcl::PointXYZRGBA &gt;</a>, <a class="el" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#ab598d8e1220f1292b938e3a66f1ec370">pcl::KdTreeFLANN&lt; pcl::InterestPoint &gt;</a> , 以及 <a class="el" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#ab598d8e1220f1292b938e3a66f1ec370">pcl::KdTreeFLANN&lt; FeatureT &gt;</a> 内被实现.</p>

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<a id="aa98483c78ce77e07454a9bfe56839cd4"></a>
<h2 class="memtitle"><span class="permalink"><a href="#aa98483c78ce77e07454a9bfe56839cd4">&#9670;&nbsp;</a></span>radiusSearch() <span class="overload">[3/3]</span></h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual int <a class="el" href="classpcl_1_1_kd_tree.html">pcl::KdTree</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::radiusSearch </td>
          <td>(</td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>index</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">double&#160;</td>
          <td class="paramname"><em>radius</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; int &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_indices</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_sqr_distances</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">unsigned int&#160;</td>
          <td class="paramname"><em>max_nn</em> = <code>0</code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span><span class="mlabel">virtual</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Search for all the nearest neighbors of the query point in a given radius (zero-copy). </p>
<dl class="section attention"><dt>注意</dt><dd>This method does not do any bounds checking for the input index (i.e., index &gt;= cloud.points.size () || index &lt; 0), and assumes valid (i.e., finite) data.</dd></dl>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">index</td><td>a <em>valid</em> index representing a <em>valid</em> query point in the dataset given by <em>setInputCloud</em>. If indices were given in setInputCloud, index will be the position in the indices vector.</td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">radius</td><td>the radius of the sphere bounding all of p_q's neighbors </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">k_indices</td><td>the resultant indices of the neighboring points </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">k_sqr_distances</td><td>the resultant squared distances to the neighboring points </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">max_nn</td><td>if given, bounds the maximum returned neighbors to this value. If <em>max_nn</em> is set to 0 or to a number higher than the number of points in the input cloud, all neighbors in <em>radius</em> will be returned. </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>返回</dt><dd>number of neighbors found in radius</dd></dl>
<dl class="exception"><dt>异常</dt><dd>
  <table class="exception">
    <tr><td class="paramname">asserts</td><td>in debug mode if the index is not between 0 and the maximum number of points </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;      {</div>
<div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;        <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_kd_tree.html#ae59a4b07f95b8193d951f940f7fb6e1d">indices_</a> == NULL)</div>
<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;        {</div>
<div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;          assert (index &gt;= 0 &amp;&amp; index &lt; <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (<a class="code" href="classpcl_1_1_kd_tree.html#a9b71072db4f7662c7c565d4c49145db2">input_</a>-&gt;points.size ()) &amp;&amp; <span class="stringliteral">&quot;Out-of-bounds error in radiusSearch!&quot;</span>);</div>
<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;          <span class="keywordflow">return</span> (<a class="code" href="classpcl_1_1_kd_tree.html#a662d9de50237121e142502a8737dfefa">radiusSearch</a> (<a class="code" href="classpcl_1_1_kd_tree.html#a9b71072db4f7662c7c565d4c49145db2">input_</a>-&gt;points[index], radius, k_indices, k_sqr_distances, max_nn));</div>
<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;        }</div>
<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;        <span class="keywordflow">else</span></div>
<div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;        {</div>
<div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;          assert (index &gt;= 0 &amp;&amp; index &lt; <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (<a class="code" href="classpcl_1_1_kd_tree.html#ae59a4b07f95b8193d951f940f7fb6e1d">indices_</a>-&gt;size ()) &amp;&amp; <span class="stringliteral">&quot;Out-of-bounds error in radiusSearch!&quot;</span>);</div>
<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;          <span class="keywordflow">return</span> (<a class="code" href="classpcl_1_1_kd_tree.html#a662d9de50237121e142502a8737dfefa">radiusSearch</a> (<a class="code" href="classpcl_1_1_kd_tree.html#a9b71072db4f7662c7c565d4c49145db2">input_</a>-&gt;points[(*<a class="code" href="classpcl_1_1_kd_tree.html#ae59a4b07f95b8193d951f940f7fb6e1d">indices_</a>)[index]], radius, k_indices, k_sqr_distances, max_nn));</div>
<div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;        }</div>
<div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#aa55c4339c96e4e406477418a1c98f289">&#9670;&nbsp;</a></span>radiusSearchT()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
<div class="memtemplate">
template&lt;typename PointTDiff &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">int <a class="el" href="classpcl_1_1_kd_tree.html">pcl::KdTree</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::radiusSearchT </td>
          <td>(</td>
          <td class="paramtype">const PointTDiff &amp;&#160;</td>
          <td class="paramname"><em>point</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">double&#160;</td>
          <td class="paramname"><em>radius</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; int &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_indices</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>k_sqr_distances</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">unsigned int&#160;</td>
          <td class="paramname"><em>max_nn</em> = <code>0</code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td> const</td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Search for all the nearest neighbors of the query point in a given radius. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">point</td><td>the given query point </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">radius</td><td>the radius of the sphere bounding all of p_q's neighbors </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">k_indices</td><td>the resultant indices of the neighboring points </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">k_sqr_distances</td><td>the resultant squared distances to the neighboring points </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">max_nn</td><td>if given, bounds the maximum returned neighbors to this value. If <em>max_nn</em> is set to 0 or to a number higher than the number of points in the input cloud, all neighbors in <em>radius</em> will be returned. </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>返回</dt><dd>number of neighbors found in radius </dd></dl>
<div class="fragment"><div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;      {</div>
<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;        <a class="code" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> p;</div>
<div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;        <a class="code" href="group__common.html#gab978bf1754771246b2f140a5b52a8f8b">copyPoint</a> (point, p);</div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;        <span class="keywordflow">return</span> (<a class="code" href="classpcl_1_1_kd_tree.html#a662d9de50237121e142502a8737dfefa">radiusSearch</a> (p, radius, k_indices, k_sqr_distances, max_nn));</div>
<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;      }</div>
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<a id="aa150954080bc14f46772871739c3bfff"></a>
<h2 class="memtitle"><span class="permalink"><a href="#aa150954080bc14f46772871739c3bfff">&#9670;&nbsp;</a></span>setEpsilon()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void <a class="el" href="classpcl_1_1_kd_tree.html">pcl::KdTree</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::setEpsilon </td>
          <td>(</td>
          <td class="paramtype">float&#160;</td>
          <td class="paramname"><em>eps</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span><span class="mlabel">virtual</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Set the search epsilon precision (error bound) for nearest neighbors searches. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">eps</td><td>precision (error bound) for nearest neighbors searches </td></tr>
  </table>
  </dd>
</dl>

<p>被 <a class="el" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#a87e5aa1e4c6a23e161712919bef9a1a7">pcl::KdTreeFLANN&lt; PointT, Dist &gt;</a>, <a class="el" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#a87e5aa1e4c6a23e161712919bef9a1a7">pcl::KdTreeFLANN&lt; pcl::VFHSignature308 &gt;</a>, <a class="el" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#a87e5aa1e4c6a23e161712919bef9a1a7">pcl::KdTreeFLANN&lt; PointTarget &gt;</a>, <a class="el" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#a87e5aa1e4c6a23e161712919bef9a1a7">pcl::KdTreeFLANN&lt; pcl::PointXYZRGBA &gt;</a>, <a class="el" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#a87e5aa1e4c6a23e161712919bef9a1a7">pcl::KdTreeFLANN&lt; pcl::InterestPoint &gt;</a> , 以及 <a class="el" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#a87e5aa1e4c6a23e161712919bef9a1a7">pcl::KdTreeFLANN&lt; FeatureT &gt;</a> 重载.</p>
<div class="fragment"><div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;      {</div>
<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;        <a class="code" href="classpcl_1_1_kd_tree.html#aa393b60f0978c529b28e060a21c96222">epsilon_</a> = eps;</div>
<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#ac105d90b2b10383adb58e62abe7b1161">&#9670;&nbsp;</a></span>setInputCloud()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">virtual void <a class="el" href="classpcl_1_1_kd_tree.html">pcl::KdTree</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::setInputCloud </td>
          <td>(</td>
          <td class="paramtype">const PointCloudConstPtr &amp;&#160;</td>
          <td class="paramname"><em>cloud</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const IndicesConstPtr &amp;&#160;</td>
          <td class="paramname"><em>indices</em> = <code>IndicesConstPtr&#160;()</code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span><span class="mlabel">virtual</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Provide a pointer to the input dataset. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">cloud</td><td>the const boost shared pointer to a <a class="el" href="classpcl_1_1_point_cloud.html" title="PointCloud represents the base class in PCL for storing collections of 3D points.">PointCloud</a> message </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">indices</td><td>the point indices subset that is to be used from <em>cloud</em> - if NULL the whole cloud is used </td></tr>
  </table>
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<p>被 <a class="el" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#aba28a792bf0c2026aa0a6a99ed3e32ec">pcl::KdTreeFLANN&lt; PointT, Dist &gt;</a>, <a class="el" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#aba28a792bf0c2026aa0a6a99ed3e32ec">pcl::KdTreeFLANN&lt; pcl::VFHSignature308 &gt;</a>, <a class="el" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#aba28a792bf0c2026aa0a6a99ed3e32ec">pcl::KdTreeFLANN&lt; PointTarget &gt;</a>, <a class="el" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#aba28a792bf0c2026aa0a6a99ed3e32ec">pcl::KdTreeFLANN&lt; pcl::PointXYZRGBA &gt;</a>, <a class="el" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#aba28a792bf0c2026aa0a6a99ed3e32ec">pcl::KdTreeFLANN&lt; pcl::InterestPoint &gt;</a> , 以及 <a class="el" href="classpcl_1_1_kd_tree_f_l_a_n_n.html#aba28a792bf0c2026aa0a6a99ed3e32ec">pcl::KdTreeFLANN&lt; FeatureT &gt;</a> 重载.</p>
<div class="fragment"><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;      {</div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;        <a class="code" href="classpcl_1_1_kd_tree.html#a9b71072db4f7662c7c565d4c49145db2">input_</a>   = cloud;</div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;        <a class="code" href="classpcl_1_1_kd_tree.html#ae59a4b07f95b8193d951f940f7fb6e1d">indices_</a> = indices;</div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#abd7f2b98e375c48d9fe113b95b3edc20">&#9670;&nbsp;</a></span>setMinPts()</h2>

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template&lt;typename PointT &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_kd_tree.html">pcl::KdTree</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::setMinPts </td>
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<p>Minimum allowed number of k nearest neighbors points that a viable result must contain. </p>
<dl class="params"><dt>参数</dt><dd>
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    <tr><td class="paramdir">[in]</td><td class="paramname">min_pts</td><td>the minimum number of neighbors in a viable neighborhood </td></tr>
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<div class="fragment"><div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;      {</div>
<div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;        <a class="code" href="classpcl_1_1_kd_tree.html#aaceb7706d5a8b57ab626277f826162d3">min_pts_</a> = min_pts;</div>
<div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#ab2c8cd07baaebb4e1504f223405419cc">&#9670;&nbsp;</a></span>setPointRepresentation()</h2>

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template&lt;typename PointT &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_kd_tree.html">pcl::KdTree</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::setPointRepresentation </td>
          <td>(</td>
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<p>Provide a pointer to the point representation to use to convert points into k-D vectors. </p>
<dl class="params"><dt>参数</dt><dd>
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    <tr><td class="paramdir">[in]</td><td class="paramname">point_representation</td><td>the const boost shared pointer to a <a class="el" href="classpcl_1_1_point_representation.html" title="PointRepresentation provides a set of methods for converting a point structs/object into an n-dimensi...">PointRepresentation</a> </td></tr>
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<div class="fragment"><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;      {</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;        <a class="code" href="classpcl_1_1_kd_tree.html#a2cacacf162468a473dca65193e708002">point_representation_</a> = point_representation;</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;        <span class="keywordflow">if</span> (!<a class="code" href="classpcl_1_1_kd_tree.html#a9b71072db4f7662c7c565d4c49145db2">input_</a>) <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;        <a class="code" href="classpcl_1_1_kd_tree.html#ac105d90b2b10383adb58e62abe7b1161">setInputCloud</a> (<a class="code" href="classpcl_1_1_kd_tree.html#a9b71072db4f7662c7c565d4c49145db2">input_</a>, <a class="code" href="classpcl_1_1_kd_tree.html#ae59a4b07f95b8193d951f940f7fb6e1d">indices_</a>);  <span class="comment">// Makes sense in derived classes to reinitialize the tree</span></div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;      }</div>
<div class="ttc" id="aclasspcl_1_1_kd_tree_html_ac105d90b2b10383adb58e62abe7b1161"><div class="ttname"><a href="classpcl_1_1_kd_tree.html#ac105d90b2b10383adb58e62abe7b1161">pcl::KdTree::setInputCloud</a></div><div class="ttdeci">virtual void setInputCloud(const PointCloudConstPtr &amp;cloud, const IndicesConstPtr &amp;indices=IndicesConstPtr())</div><div class="ttdoc">Provide a pointer to the input dataset.</div><div class="ttdef"><b>Definition:</b> kdtree.h:88</div></div>
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